Molecular dissection of developmental behavior of tiller number and plant height and their relationship in rice (Oryza sativa L.)

Plant height and tiller number are two important characters related to yield in rice (Oriza sativa L.). Zhenshan97 x Minghui63 recombinant inbred lines were employed to dissect the genetic basis of development of plant height and tiller number using conditional and unconditional composite interval mapping approaches. The traits were normally distributed with transgressive segregation in both directions. Increasingly negative correlations were observed between tiller number and plant height at five consecutive growth stages. A total of 23 and 24 QTL were identified for tiller number and plant height, respectively. More QTL were detected by conditional mapping than by conventional mapping. Different QTL/genes apparently controlled the traits at different developmental stages. Three genomic regions were identified as putative co-located QTL, which showed opposite additive effects on tiller number and plant height. Furthermore, in the period reaching maximum tiller number, the expression of QTL for tiller number was active, whereas that of QTL for plant height was inactive. These facts provided a possible genetic explanation for the negative correlations between the traits. The research demonstrates conditional mapping to be superior to conventional mapping for this type of research. Implications of the results for hybrid rice improvement are discussed.     

Mapping of QTL for tiller number at different stages of growth in wheat using double haploid and immortalized F<Subscript>2</Subscript> populations

Effective tiller number is one of the most important traits for wheat (Triticum aestivum L.) yield, but the inheritance of tillering is poorly understood. A set of 168 doubled haploid (DH) lines derivatives of a cross between two winter wheat cultivars (Huapei 3 and Yumai 57), and an immortalized F₂ (IF₂) population generated by randomly permutated intermating of these DHs were investigated, and QTLs of tillering related to the maximum tillering of pre-winter (MTW), maximum tillering in spring (MTS), and effective tillering in harvest (ETH) were mapped. Phenotypic data were collected for the two populations from two different environments. Using inclusive composite interval mapping (ICIM), a total of 9 and 18 significant QTL were detected across environments for tillering in the DH and IF₂ populations, respectively. Four QTLs were common between two populations. A major QTL located on the 5D chromosome with the allele originating from Yumai 57 was detected and increased 1.92 and 3.55 tillers in MTW and MTS, respectively. QTLs (QMts6D, QEth6D) having a neighbouring marker interval at Xswes679.1 and Xcfa2129 on chromosome 6D was detected in MTS and ETH. These results provide a better understanding of the genetic factors for selectively expressing the control of tiller number in different growth stages and facilitate marker-assisted selection strategy in breeding.     

Look before you leap: a new approach to mapping QTL

In this paper, we present an innovative and powerful approach for mapping quantitative trait loci (QTL) in experimental populations. This deviates from the traditional approach of (composite) interval mapping which uses a QTL profile to simultaneously determine the number and location of QTL. Instead, we look before we leap by employing separate detection and localization stages. In the detection stage, we use an iterative variable selection process coupled with permutation to identify the number and synteny of QTL. In the localization stage, we position the detected QTL through a series of one-dimensional interval mapping scans. Results from a detailed simulation study and real analysis of wheat data are presented. We achieve impressive increases in the power of QTL detection compared to composite interval mapping. We also accurately estimate the size and position of QTL. An R library, DLMap, implements the methods described here and is freely available from CRAN ().     

小麦苗期水分利用效率及其相关性状的QTL分析

以小麦DH群体(旱选10号×鲁麦14)为研究材料,采用复合区间作图法,对小麦幼苗在水分胁迫及非胁迫条件下的水分利用效率(WUE)及其相关性状的QTL进行定位,并对比分析QTL的加性效应.两种水分条件下共检测到14个具显著加性效应的QTL,分布在2A、3A、4A、5A、6A、7A、1B、3B、3D染色体上,可解释表型变异的范围在6.36%～19.73%.其中,非胁迫(对照)条件下检测到10个QTL,包括2个单株WUE的QTL,5个地上部WUE的QTL,1个根系WUE的QTL及2个总耗水量的QTL;水分胁迫条件下上述性状各检测到1个QTL.对于同一性状没有检测到在两种水分条件下均位于同一标记区间的QTL,表明不同水分环境条件下同一性状的QTL表达模式是不同的.论文也讨论了可能用于标记辅助选择的QTL及其分子标记.     

A new intervarietal linkage map and its application for quantitative trait locus analysis of "gigas" features in bread wheat.

A doubled-haploid (DH) population from an intervarietal cross between the Japanese cultivar 'Fukuho-komugi' and the Israeli wheat line 'Oligoculm' was produced by means of wheat x maize crosses. One hundred seven DH lines were genotyped to construct a simple sequence repeat (SSR) based linkage map with RFLP, RAPD, and inter-simple sequence repeat markers. Out of 570 loci genotyped, 330 were chosen based on their positions on the linkage map to create a "framework" map for quantitative trait locus (QTL) analysis. Among the 28 linkage groups identified, 25 were assigned to the 21 chromosomes of wheat. The total map length was 3948 cM, including the three unassigned linkage groups (88 cM), and the mean interval between loci was 12.0 cM. Loci with segregation distortion were clustered on chromosomes 1A, 4B, 4D, 5A, 6A, 6B, and 6D. After vernalization, the DH lines were evaluated for spike number per plant (SN) and spike length (SL) in a greenhouse under 24-h daylength to assess the "gigas" features (extremely large spikes and leaves) of 'Oligoculm'. The DH lines were also autumn-sown in the field in two seasons (1990-1991 and 1997-1998) for SN and SL evaluation. QTL analysis was performed by composite interval mapping (CIM) with the framework map to detect QTLs for SN and SL. A major QTL on 1AS, which was stable in both greenhouse and field conditions, was found to control SN. This QTL was close to the glume pubescence locus (Hg) and explained up to 62.9% of the total phenotypic variation. The 'Oligoculm' allele restricted spike number. The SSR locus Xpsp2999 was the closest locus to this QTL and is considered to be a possible marker for restricted tillering derived from 'Oligoculm'. Eight QTLs were detected for SL. The largest QTL detected on 2DS was common to the greenhouse and field environments. It explained up to 33.3% of the total phenotypic variation. The second largest QTL on 1AS was common to the greenhouse and the 1997-1998 season. The position of this QTL was close to that for the SN detected on 1AS. The association between SN and SL is discussed.     

大白菜部分形态性状的QTL定位与分析

应用352个标记位点的大白菜AFLP和RAPD图谱和一套栽培品种间杂交获得的重组自交系群体,采用复合区间作图的方法对大白菜9个形态性状进行QTL定位及遗传效应研究。在14个连锁群上检测到50个QTL:其中控制株型的QTL有5个;控制株高的QTL有6个;控制开展度的QTL有5个;控制最大叶长的QTL有7个;控制最大叶宽的QTL有4个;控制叶形指数的QTL有6个;控制中肋长的QTL有7个;控制中肋宽的QTL有4个;控制抽苔的QTL有6个。另外,估算了单个QTL的遗传贡献率和加性效应。这将为大白菜品种改良中形态性状的分子标记辅助选择提供理论依据。     

Quantifying evidence for candidate gene polymorphisms: Bayesian analysis combining sequence-specific and quantitative trait loci colocation information

We calculate posterior probabilities for candidate genes as a function of genomic location. Posterior probabilities for quantitative trait loci (QTL) presence in a small interval are calculated using a Bayesian model-selection approach based on the Bayesian information criterion (BIC) and used to combine QTL colocation information with sequence-specific evidence, e.g., from differential expression and/or association studies. Our method takes into account uncertainty in estimation of number and locations of QTL and estimated map position. Posterior probabilities for QTL presence were calculated for simulated data with n = 100, 300, and 1200 QTL progeny and compared with interval mapping and composite-interval mapping. Candidate genes that mapped to QTL regions had substantially larger posterior probabilities. Among candidates with a given Bayes factor, those that map near a QTL are more promising for further investigation with association studies and functional testing or for use in marker-aided selection. The BIC is shown to correspond very closely to Bayes factors for linear models with a nearly noninformative Zellner prior for the simulated QTL data with n > or = 100. It is shown how to modify the BIC to use a subjective prior for the QTL effects.     

Quantitative trait loci controlling phenotypes related to the perennial versus annual habit in wild relatives of maize

We used quantitative trait locus/loci (QTL) mapping to study the inheritance of traits associated with perennialism in a cross between an annual (Zea mays ssp. parviglumis) and a perennial (Z. diploperennis) species of teosinte. The most striking difference between these species is that Z. diploperennis forms rhizomes, whereas Z. mays ssp. parviglumis lacks these over-wintering underground stems. An F₂ population of 425 individuals was genotyped at 95 restriction fragment length polymorphism marker loci and the association between phenotype and genotype was analyzed by composite interval mapping. We detected a total of 38 QTL for eight traits. The number of QTL found for each trait ranged from two for rhizome formation to nine for tillering. QTL for six of the traits mapped near each other on chromosome 2, and QTL for four traits mapped near each other on chromosome 6, suggesting that these regions play an important role in the evolution of the perennial habit in teosinte. Most of the 38 QTL had small effects, and no single QTL showed a strikingly large effect. The map positions that we determined for rhizome formation and other traits in teosinte may help to locate corresponding QTL in pasture and turf grasses used as forage for cattle and for erosion control in agro-ecosystems.     

Quantitative Trait Loci Mapping of Leaf Morphological Traits and Chlorophyll Content in Cultivated Tetraploid Cotton

Genetic mapping provides a powerful tool for quantitative trait loci （QTL） analysis at the molecular level. A simple sequence repeat （SSR） genetic map containing 590 markers and a BCI population from two cultivated tetraploid cotton （Gossypium hirsutum L.） cultivars, namely TM-1 and Hai 7124 （G. barbadense L.）, were used to map and analyze QTL using the composite interval mapping （CIM） method. Thirty one QTLs, 10 for lobe length, 13 for lobe width, six for lobe angle, and two for leaf chlorophyll content, were detected on 15 chromosomes or linkage groups at logarithm of odds （LOD）≥2.0, of which 15 were found for leaf morphology at LOD≥3.0. The genetic effects of the QTL were estimated. These results are fundamental for marker-assisted selection （MAS） of these traits in tetraploid cotton breeding.     

植物QTL分析的理论研究进展

数量性状的表型是由数量性状基因座 ( Quantitative trait locus,QTL)和环境效应共同作用的结果。传统的数量遗传学采用统计学的方法由一级统计量和二级统计量描述处理 QTL的复合作用 ,估计各种遗传参数 (例如遗传力、遗传相关、遗传进度、有效因子数等 ) ,用于指导遗传育种实践。然而 ,在传统的数量遗传学分析中 ,往往假设数量性状受微效多基因控制 ,这些基因具有相同的并且是较微小的效应 ,所估计的遗传参数反映的是数量性状多基因系统的整体特征 ,其理论方法不能用于追踪研究和描述单个数量性状基因的作用。近年来 ,由于分子生物学技…     

A whole genome scan to detect quantitative trait loci for gestation length and sow maternal ability related traits in a White Duroc × Erhualian F2 resource population

Gestation length and maternal ability are important to improve the sow reproduction efficiency and their offspring survival. To map quantitative trait loci (QTL) for gestation length and maternal ability related traits including piglet survival rate and average body weight of piglets at weaning, more than 200 F2 sows from a White Duroc × Erhualian resource population were phenotyped. A genome-wide scan was performed with 194 microsatellite markers covering the whole pig genome. QTL analysis was carried out using a composite regression interval mapping method via QTL express. The results showed that total number of born piglets was significantly correlated with gestation length (r = -0.13, P < 0.05). Three QTL were detected on pig chromosome (SSC)2, 8 and 12 for gestation length. The QTL on SSC2 achieved the 5% genome-wide significant level and the QTL on SSC8 was consistent with previous reports. Four suggestive QTL were identified for maternal ability related traits including 1 QTL for survival rate of piglets at weaning on SSC8, 3 QTL for average body weight of piglet at weaning on SSC3, 11 and 13.     

Quantitative trait locus (QTL) isogenic recombinant analysis: a method for high-resolution mapping of QTL within a single population

In the quest for fine mapping quantitative trait loci (QTL) at a subcentimorgan scale, several methods that involve the construction of inbred lines and the generation of large progenies of such inbred lines have been developed (Complex Trait Consortium 2003). Here we present an alternative method that significantly speeds up QTL fine mapping by using one segregating population. As a first step, a rough mapping analysis is performed on a small part of the population. Once the QTL have been mapped to a chromosomal interval by standard procedures, a large population of 1000 plants or more is analyzed with markers flanking the defined QTL to select QTL isogenic recombinants (QIRs). QIRs bear a recombination event in the QTL interval of interest, while other QTL have the same homozygous genotype. Only these QIRs are subsequently phenotyped to fine map the QTL. By focusing at an early stage on the informative individuals in the population only, the efforts in population genotyping and phenotyping are significantly reduced as compared to prior methods. The principles of this approach are demonstrated by fine mapping an erucic acid QTL of rapeseed at a subcentimorgan scale.     

水稻叶片水势的QTL定位与候选基因分析

为探究叶片水势(LWP)相关基因在水稻(Oryza sativa)抗旱中的作用及其遗传机制,以热研2号(Nekken2)和华占(HZ)为亲本以及构建的120个重组自交系(RILs)群体为实验材料,对水稻分蘖期叶片水势进行检测,并利用前期基于高通量测序构建的分子遗传连锁图谱进行数量性状基因座(QTL)分析。结果表明,共检...     

Functional mapping of quantitative trait loci associated with rice tillering

Several biologically significant parameters that are related to rice tillering are closely associated with rice grain yield. Although identification of the genes that control rice tillering and therefore influence crop yield would be valuable for rice production management and genetic improvement, these genes remain largely unidentified. In this study, we carried out functional mapping of quantitative trait loci (QTLs) for rice tillering in 129 doubled haploid lines, which were derived from a cross between IR64 and Azucena. We measured the average number of tillers in each plot at seven developmental stages and fit the growth trajectory of rice tillering with the Wang–Lan–Ding mathematical model. Four biologically meaningful parameters in this model––the potential maximum for tiller number (K), the optimum tiller time (t ₀), and the increased rate (r), or the reduced rate (c) at the time of deviation from t ₀––were our defined variables for multi-marker joint analysis under the framework of penalized maximum likelihood, as well as composite interval mapping. We detected a total of 27 QTLs that accounted for 2.49–8.54% of the total phenotypic variance. Nine common QTLs across multi-marker joint analysis and composite interval mapping showed high stability, while one QTL was environment-specific and three were epistatic. We also identified several genomic segments that are associated with multiple traits. Our results describe the genetic basis of rice tiller development, enable further marker-assisted selection in rice cultivar development, and provide useful information for rice production management.     

Quantitative trait locus mapping with background control in genetic populations of clonal F1 and double cross

In this study, we considered five categories of molecular markers in clonal F₁ and double cross populations, based on the number of distinguishable alleles and the number of distinguishable genotypes at the marker locus. Using the completed linkage maps, incomplete and missing markers were imputed as fully informative markers in order to simplify the linkage mapping approaches of quantitative trait genes. Under the condition of fully informative markers, we demonstrated that dominance effect between the female and male parents in clonal F₁ and double cross populations can cause the interactions between markers. We then developed an inclusive linear model that includes marker variables and marker interactions so as to completely control additive effects of the female and male parents, as well as the dominance effect between the female and male parents. The linear model was finally used for background control in inclusive composite interval mapping (ICIM) of quantitative trait locus (QTL). The efficiency of ICIM was demonstrated by extensive simulations and by comparisons with simple interval mapping, multiple‐QTL models and composite interval mapping. Finally, ICIM was applied in one actual double cross population to identify QTL on days to silking in maize.     

Mapping resistance to cereal aphids in barley

 A set of 150 doubled-haploid (DH) barley (Hordeum vulgare L.) lines derived from the cross of Harrington/TR306 was used to determine the number and chromosomal location of quantitative trait loci (QTLs) controlling resistance to cereal aphids. The experiments were conducted under natural infestation in the field during two growing seasons: 1994 and 1995. Aphid resistance was measured by counting the number of aphids per plot. Counts were made on a weekly basis. Each year at the time of maximum aphid density there was an obvious difference in reaction between the parental genotypes. The DH lines showed continuous variation for aphid density. Simple interval mapping and simplified composite interval mapping revealed that the principal QTL determining cereal aphid resistance is on the distal region of the short arm of chromosome 1. This aphid-resistance QTL is linked with a heading-date QTL. At the time of highest aphid infestation, this QTL accounted for 31% and 22% of the total variance of aphid density in 1994 and 1995, respectively. A QTL on chromosome 5 was also detected but only by simplified composite interval mapping. However, the largest consistent effect was due to the QTL on the short arm of chromosome 1. This QTL may be a useful target for marker-assisted selection for adult plant cereal aphid resistance in barley. Received: 10 September 1996/Accepted: 11 October 1996     

Unconditional and conditional QTL mapping for the developmental behavior of tiller number in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A single segment substitution population of 26 lines and their recipient parent Hua-jing-xian 74 (HJX74) were selected as experimental materials for analyzing the developmental behavior of tiller number in rice. By the unconditional QTL (quantitative trait locus) mapping method, a total number of 14 SSSLs were detected with QTLs controlling rice tiller number. The number of QTLs significantly affecting tiller number and their effect values estimated differed across measuring stages. More QTLs could be detected based on time-dependent measures of different stages. By the conditional QTL mapping method, it is possible to reveal net expression of gene in a time interval. 14 QTLs on tiller number expressed their effects in dynamic patterns of themselves during whole ontogeny. They exhibited mainly negative effects within 7 days after transplanting. During 7–21 days, QTLs were in active status and expressed larger positive effects. In the mid-period of 21–35 days, they had opposite genetic effects to wither tillers. Since then these QTLs expressed positive effects again to cause the appearance of noneffective tillers. The dynamics of QTL effects was in agreement with the actual change of tillers. Mapping QTL combining unconditional with conditional analysis for time-dependent measures is helpful to understand roundly the genetic bases for the development of quantitative traits.     

The effect of an imprecise map on interval mapping QTLs

The statistical analysis of quantitative trait locus (QTL) experiments relies on the use of a linkage map of the markers genotyped. Such a map is, at best, a good estimate of the true map. Resources might be diverted into developing better marker maps or improved maps become available after the analysis, raising concerns over the original analysis. It is therefore important to understand the sensitivity of QTL analysis to map inaccuracy. We have used simulation methods to investigate the consequences of an incorrect map on the results of a QTL analysis using interval mapping. Backcross data sets were generated with a particular map and then analysed with both the correct map and incorrect maps. If the incorrect maps maintained the true linkage groups (i.e. no markers were incorrectly assigned to another linkage group), the accuracy of the map had little or no impact on the ability to detect QTLs, the true significance levels of the tests or the relative placement of QTLs. When a marker was incorrectly placed on another linkage group, there was a small increase in the level of the test. After adjusting for this increase, there was a decrease in power to detect a QTL near the misplaced marker. This decrease was of a similar magnitude to that found when using a single-marker analysis compared with interval mapping. These results mean that QTL analyses can proceed without the need for very accurate marker maps, and that estimated QTL positions can be translated onto updated maps without the need for reanalysis.     

Mapping QTL for grain yield, yield components, and spike features in a doubled haploid population of bread wheat

A doubled haploid (DH) population derived from a cross between the Japanese cultivar 'Fukuho-kumogi' and the Israeli wheat line 'Oligoculm' was used to map genome regions involved in the expression of grain yield, yield components, and spike features in wheat (Triticum aestivum L). A total of 371 markers (RAPD, SSR, RFLP, AFLP, and two morphological traits) were used to construct the linkage map that covered 4190 cM of wheat genome including 28 linkage groups. The results of composite interval mapping for all studied traits showed that some of the quantitative trait loci (QTL) were stable over experiments conducted in 2004 and 2005. The major QTL located in the Hair-Xpsp2999 interval on chromosome 1A controlled the expression of grains/spike (R(2) = 12.9% in 2004 and 22.4% in 2005), grain weight/spike (R(2) = 21.4% in 2004 and 15.8% in 2005), and spike number (R(2) = 15.6% in 2004 and 5.4% in 2005). The QTL for grain yield located on chromosomes 6A, 6B, and 6D totally accounted for 27.2% and 31.7% of total variation in this trait in 2004 and 2005, respectively. Alleles inherited from 'Oligoculm' increased the length of spikes and had decreasing effects on spike number. According to the data obtained in 2005, locus Xgwm261 was associated with a highly significant spike length QTL (R(2) = 42.33%) and also the major QTL for spikelet compactness (R(2) = 26.1%).     

Genetic basis of adaptation: flowering time in Arabidopsis thaliana

 We have mapped QTLs (quantitative trait loci) for an adaptive trait, flowering time, in a selfing annual, Arabidopsis thaliana. To obtain a mapping population we made a cross between an early-summer, annual strain, Li-5, and an individual from a late over-wintering natural population, Naantali. From the backcross to Li-5 298 progeny were grown, of which 93 of the most extreme individuals were genotyped. The data were analysed with both interval mapping and composite interval mapping methods to reveal one major and six minor QTLs, with at least one QTL on each of the five chromosomes. The QTL on chromosome 4 was a major one with an effect of 17.3 days on flowering time and explaining 53.4% of the total variance. The others had effects of at most 6.5 days, and they accounted for only small portions of the variance. Epistasis was indicated between one pair of the QTLs. The result of finding one major QTL and little epistasis agrees with previous studies on flowering time in Arabidopsis thaliana and other species. That several QTLs were found was expected considering the large number of possible candidate loci. In the light of the suggested genetic models of gene action at the candidate loci, epistasis was to be expected. The data showed that major QTLs for adaptive traits can be detected in non-domesticated species. Received: 15 January 1997/Accepted: 21 February 1997